Description
Who should attend
This course is intended for:
- Data engineers
- Data architects
- Database architects
- Database administrators
- Database developers
Prerequisites
We recommend that attendees of this course have completed the following courses:
- Fundamentals of Analytics on AWS – Part 1 (Digital course)
- Fundamentals of Analytics on AWS – Part 2 (Digital course)
- Building Data Lakes on AWS Building Data Lakes on AWS (BDLA) (Instructor led Training)
- Building Data Analytics Solutions Using Amazon Redshift Building Data Analytics Solutions Using Amazon Redshift (BDASAR) (Instructor led Training)
Course Objectives
In this course, you will learn to:
- Describe Amazon Redshift architecture and its roles in a modern data architecture
- Design and implement a data warehouse in the cloud using Amazon Redshift
- Identify and load data into an Amazon Redshift data warehouse from a variety of sources
- Analyze data using SQL QEV2 notebooks
- Design and implement a disaster recovery strategy for an Amazon Redshift data warehouse
- Perform maintenance and performance tuning on an Amazon Redshift data warehouse
- Secure and manage access to an Amazon Redshift data warehouse
- Share data between multiple Redshift clusters in an organization
- Orchestrate workflows in the data warehouse using AWS Step Functions state machines
- Create an ML model and configure predictors using Amazon Redshift ML
Outline: Data Warehousing on AWS (DWAWS)
Day 1
Module 1: Data Warehouse Concepts
- Modern data architecture
- Introduction to the course story
- Data warehousing with Amazon Redshift
- Amazon Redshift Serverless architecture
- Hands-On Lab: Launch and Configure an Amazon Redshift Serverless Data Warehouse
Module 2: Setting up Amazon Redshift
- Data models for Amazon Redshift
- Data management in Amazon Redshift
- Managing permissions in Amazon Redshift
- Hands-On Lab: Setting up a Data Warehouse using Amazon Redshift Serverless
Module 3: Loading Data
- Overview of data sources
- Loading data from Amazon Simple Storage Service (Amazon S3)
- Extract, transform, and load (ETL) and extract, load, and transform (ELT)
- Loading streaming data
- Loading data from relational databases
- Hands-On Lab: Populating the data warehouse
Day 2
Module 4: Deep Dive into SQL Query Editor v2 and Notebooks
- Features of Amazon Redshift Query Editor v2
- Demonstration: Using Amazon Redshift Query Editor v2
- Advanced queries
- Hands-On Lab: Data Wrangling on AWS
Module 5: Backup and Recovery
- Disaster recovery
- Backing up and restoring Amazon Redshift provisioned
- Backing up and restoring Amazon Redshift Serverless
Module 6: Amazon Redshift Performance Tuning
- Factors that impact query performance
- Table maintenance and materialized views
- Query analysis
- Workload management
- Tuning guidance
- Amazon Redshift monitoring
- Hands-On Lab: Performance Tuning the Data Warehouse
Module 7: Securing Amazon Redshift
- Introduction to Amazon Redshift security and compliance
- Authentication with Amazon Redshift
- Access control with Amazon Redshift
- Data encryption with Amazon Redshift
- Auditing and compliance with Amazon Redshift
- Hands-On Lab: Securing Amazon Redshift
Day 3
Module 8: Orchestration
- Overview of data orchestration
- Orchestration with AWS Step Functions
- Orchestration with Amazon Managed Workflows for Apache Airflow (MWAA)
- Hands-On Lab: Orchestrating the Data Warehouse Pipeline
Module 9: Amazon Redshift ML
- Machine Learning Overview
- Getting started with Amazon Redshift ML
- Amazon Redshift ML workflow scenarios
- Amazon Redshift ML Usage
- Hands-On Lab: Predicting customer churn with Amazon Redshift ML
Module 10: Amazon Redshift Data Sharing
- Overview of data sharing in Amazon Redshift
- Amazon DataZone for Data as a service
Module 11: Wrap-Up
- Hands-On Lab: End of course challenge lab
